All Classes Interface Summary Class Summary Enum Summary Exception Summary
| Class |
Description |
| AAttribute |
|
| AbstractDyadScaler |
A scaler that can be fit to a certain dataset and then be used to standardize
datasets, i.e. transform the data to have a mean of 0 and a standard
deviation of 1 according to the data it was fit to.
|
| ACollectionOfObjectsAttribute<O> |
|
| ActiveDyadRanker |
Abstract description of a pool-based active learning strategy for dyad
ranking.
|
| ADataset<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance> |
|
| ADyadRankedNodeQueue<N,V extends java.lang.Comparable<V>> |
A queue whose elements are nodes, sorted by a dyad ranker.
|
| ADyadRankedNodeQueueConfig<N> |
A configuration for a dyad ranked node queue.
|
| ADyadRankingInstance |
|
| AFileSamplingAlgorithm |
An abstract class for file-based sampling algorithms providing basic
functionality of an algorithm.
|
| AFilter |
|
| AGeneralDatasetBackedDataset<E extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance> |
|
| AGenericObjectAttribute<O> |
|
| AggregatingPredictionPerformanceMeasure<E,A> |
|
| AHomogeneousPredictionPerformanceMeasure<O> |
|
| AInstance |
|
| AInstanceMeasure<O,P> |
|
| ALabeledDataset<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance> |
|
| AMinimumDistanceSearchStrategy |
Abstract class for minimum distance search strategies.
|
| AMonteCarloCrossValidationBasedEvaluatorFactory<F extends AMonteCarloCrossValidationBasedEvaluatorFactory<F>> |
An abstract factory for configuring Monte Carlo cross-validation based evaluators.
|
| AMultiLabelClassificationMeasure |
|
| APredictionPerformanceMeasure<E,A> |
|
| AProcessListener |
The process listener may be attached to a process in order to handle its ouputs streams in a controlled way.
|
| ARandomlyInitializingDyadRanker |
|
| ARankingAttribute<O> |
|
| ARankingPredictionPerformanceMeasure |
|
| ARegressionMeasure |
|
| ArffDatasetAdapter |
|
| ArffUtilities |
Utility class for handling Arff dataset files.
|
| ASampleAlgorithmFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>,A extends ASamplingAlgorithm<D>> |
|
| ASamplingAlgorithm<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
An abstract class for sampling algorithms providing basic functionality of an
algorithm.
|
| ASimplifiedTSClassifier<T> |
Simplified batch-learning time series classifier which can be trained and
used as a predictor.
|
| ASimplifiedTSCLearningAlgorithm<T,C extends ASimplifiedTSClassifier<T>> |
|
| ASingleLabelClassifier |
|
| ASupervisedLearner<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends I>,P extends org.api4.java.ai.ml.core.evaluation.IPrediction,B extends org.api4.java.ai.ml.core.evaluation.IPredictionBatch> |
|
| AThresholdBasedMultiLabelClassificationMeasure |
|
| ATimeseriesAttribute<O> |
|
| ATimeSeriesClassificationModel<L,D extends TimeSeriesDataset> |
Time series classifier which can be trained and used as a predictor.
|
| ATSCAlgorithm<Y,D extends TimeSeriesDataset,C extends ATimeSeriesClassificationModel<Y,D>> |
|
| AttributeBasedStratiAmountSelectorAndAssigner |
This class is responsible for computing the amount of strati in
attribute-based stratified sampling and assigning elements to the strati.
|
| AttributeDiscretizationPolicy |
|
| AutoMEKAGGPFitnessMeasureLoss |
Measure combining exact match, hamming loss, f1macroavgL and rankloss.
|
| AveragingPredictionPerformanceMeasure<E,A> |
|
| BilinFunction |
Wraps the NLL optimizing problem into the QNMinimizer optimizer.
|
| BiliniearFeatureTransform |
Implementation of the feature transformation method using the Kroenecker
Product.
|
| BOSSClassifier |
|
| BOSSEnsembleClassifier |
|
| BOSSLearningAlgorithm |
|
| BOSSLearningAlgorithm.IBossAlgorithmConfig |
|
| CaseControlLikeSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> |
|
| CheckedJaicoreMLException |
|
| ChoquisticRelevanceLoss |
|
| ClassifierWeightedSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> |
The idea behind this Sampling method is to weight instances depended on the
way a pilot estimator p classified them.
|
| ClassMapper |
Class mapper used for predictions of String objects which are internally
predicted by time series classifiers as ints.
|
| ClassStratiFileAssigner |
|
| ClusterableDataset |
|
| ClusterSampling<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance & org.apache.commons.math3.ml.clustering.Clusterable,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>> |
|
| ClusterStratiAssigner |
|
| ConfigurationException |
|
| ConfigurationLearningCurveExtrapolationEvaluator |
Predicts the accuracy of a classifier with certain configurations on a point
of its learning curve, given some anchorpoint and its configurations using
the LCNet of pybnn
Note: This code was copied from LearningCurveExtrapolationEvaluator and
slightly reworked
|
| ConfigurationLearningCurveExtrapolator |
This class is a subclass of LearningCurveExtrapolator which deals
with the slightly different setup that is required by the LCNet
of pybnn
|
| ConstantSplitSetGenerator<I extends org.api4.java.ai.ml.core.dataset.IInstance,D extends org.api4.java.ai.ml.core.dataset.IDataset<? extends I>> |
|
| Dataset |
|
| DatasetCapacityReachedException |
|
| DatasetDeriver<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
|
| DatasetFileSorter |
Sorts a Dataset file with a Mergesort.
|
| DatasetPropertyComputer |
|
| DatasetSplitSet<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
|
| DatasetUtil |
|
| DefaultProcessListener |
The DefaultProcessListener might be used to forward any type of outputs of a process to a logger.
|
| DenseDyadRankingInstance |
|
| DenseInstance |
|
| DFT |
|
| DiscretizationHelper |
This helper class provides methods that are required in order to discretize
numeric attributes.
|
| DiscretizationHelper.DiscretizationStrategy |
|
| Dyad |
Represents a dyad consisting of an instance and an alternative, represented
by feature vectors.
|
| DyadDatasetPoolProvider |
|
| DyadMinMaxScaler |
A scaler that can be fit to a certain dataset and then be used to normalize
dyad datasets, i.e. transform the data such that the values of each feature
lie between 0 and 1.
|
| DyadRankingAttribute |
|
| DyadRankingAttributeValue |
|
| DyadRankingDataset |
A dataset representation for dyad ranking.
|
| DyadRankingFeatureTransformNegativeLogLikelihood |
Implements the negative log-likelihood function for the feature
transformation Placket-Luce dyad ranker.
|
| DyadRankingFeatureTransformNegativeLogLikelihoodDerivative |
Represents the derivate of the negative log likelihood function in the
context of feature transformation Placket-Luce dyad ranking [1].
|
| DyadStandardScaler |
A scaler that can be fit to a certain dataset and then be used to standardize
datasets, i.e. transform the data to have a mean of 0 and a standard
deviation of 1 according to the data it was fit to.
|
| DyadUnitIntervalScaler |
A scaler that can be fit to a certain dataset and then be used to normalize
datasets, i.e. transform the data to have a length of 1.
|
| EAggregatedClassifierMetric |
|
| EArffAttributeType |
|
| EArffItem |
|
| EarlyAbandonMinimumDistanceSearchStrategy |
Class implementing a search strategy used for finding the minimum distance of
a Shapelet object to a time series. |
| EClassificationPerformanceMeasure |
|
| ERegressionPerformanceMeasure |
|
| ErrorRate |
|
| EvaluationException |
|
| ExactMatch |
|
| ExhaustiveMinimumDistanceSearchStrategy |
Class implementing a search strategy used for finding the minimum distance of
a Shapelet object to a time series. |
| ExtrapolatedSaturationPointEvaluator |
For the classifier a learning curve will be extrapolated with a given set of
anchorpoints.
|
| ExtrapolatedSaturationPointEvaluatorFactory |
|
| ExtrapolationRequest |
This class describes the request that is sent to an Extrapolation Service.
|
| ExtrapolationServiceClient<C> |
This class describes the client that is responsible for the communication
with an Extrapolation Service.
|
| F1MacroAverageL |
|
| F1Measure |
|
| FalseNegatives |
|
| FalsePositives |
|
| FeatureTransformPLDyadRanker |
A feature transformation Plackett-Luce dyad ranker.
|
| FileDatasetDescriptor |
|
| FileDatasetDescriptor |
|
| FilterBasedDatasetSplitter<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
|
| FixedDataSplitSetGenerator<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
This is an IDatasetSplitSetGenerator that produces splits for one initially fixed dataset.
|
| FixedSplitClassifierEvaluator |
|
| FMeasure |
|
| FStat |
F-Stat quality measure performing a analysis of variance according to chapter
3.2 of the original paper.
|
| GMeans<C extends org.apache.commons.math3.ml.clustering.Clusterable> |
Implementation of Gmeans based on Helen Beierlings implementation of
GMeans(https://github.com/helebeen/AILibs/blob/master/JAICore/jaicore-modifiedISAC/src/main/java/jaicore/modifiedISAC/ModifiedISACgMeans.java).
For more Information see: "Hamerly, G., and Elkan, C. 2003.
|
| GmeansSampling<I extends IClusterableInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>> |
Implementation of a sampling method using gmeans-clustering.
|
| GmeansSamplingFactory<I extends IClusterableInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>> |
|
| GMeansStratiAmountSelectorAndAssigner |
Combined strati amount selector and strati assigner via g-means.
|
| Group<C,I> |
Group.java - Stores a group with it center as ID and the associated instances
|
| GroupIdentifier<C> |
|
| Hamming |
|
| HammingMassFunction |
|
| HistogramBuilder |
|
| IClusterableInstance |
|
| IConfigurableLabelRanker |
|
| IDatasetFilter |
|
| IDyadFeatureTransform |
|
| IDyadRanker |
An abstract representation of a dyad ranker.
|
| IDyadRankingFeatureTransformPLGradientDescendableFunction |
An interface for a differentiable function in the context of feature
transformation Placket-Luce dyad ranking.
|
| IDyadRankingFeatureTransformPLGradientFunction |
Represents a differentiable function in the context of dyad ranking based on
feature transformation Placket-Luce models.
|
| IDyadRankingPoolProvider |
Interface for an active learning pool provider in the context of dyad
ranking.
|
| IFilter |
|
| IGroupBasedRanker<O,I extends org.api4.java.ai.ml.ranking.dataset.IRankingInstance<O>,D extends org.api4.java.ai.ml.ranking.dataset.IRankingDataset<O,I>,Z> |
|
| IGroupBuilder<C,I> |
IGroupBuilder discribes the act of building groups out of probleminstances
|
| IGroupSolutionRankingSelect<C,S,I,P> |
|
| IInstanceCollector<I> |
|
| IMassFunction |
|
| IMultiClassClassificationExperimentConfig |
|
| InconsistentDataFormatException |
|
| INDArrayDyadRankingInstance |
|
| INDArrayTimeseries |
|
| InputOptimizerLoss |
|
| InputOptListener |
|
| InstanceSchema |
|
| InstanceWiseF1 |
Instance-wise F1 measure for multi-label classifiers.
|
| IntBasedCategoricalAttribute |
|
| IntBasedCategoricalAttributeValue |
|
| InvalidAnchorPointsException |
Exception that is thrown, when the anchorpoints generated for learning curve
extrapolation are not suitable.
|
| InversePowerLawConfiguration |
This class encapsulates the three parameters that are required in order to
create a Inverse Power Law function.
|
| InversePowerLawExtrapolationMethod |
This class describes a method for learning curve extrapolation which
generates an Inverse Power Law function.
|
| InversePowerLawLearningCurve |
Representation of a learning curve with the Inverse Power Law function, which has three parameters named a, b and c.
|
| IOWAValueFunction |
|
| IPLDyadRanker |
An abstract representation for a dyad ranker using Placket Luce models.
|
| IPLNetDyadRankerConfiguration |
|
| IProcessListener |
|
| IQualityMeasure |
Interface for a quality measure assessing distances of instances to a
shapelet given the corresponding class values.
|
| IRankedSolutionCandidateProvider<I,S> |
|
| IRerunnableSamplingAlgorithmFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>,A extends ASamplingAlgorithm<D>> |
Extension of the ISamplingAlgorithmFactory for sampling algorithms that can
re-use informations from a previous run of the Sampling algorithm.
|
| IRPCConfig |
|
| ISamplingAlgorithmFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>,A extends ASamplingAlgorithm<D>> |
Interface for a factory, which creates a sampling algorithm.
|
| ISQLDatasetMapper |
This interface is meant to offer the ability to serialize and unserialize datasets from and to database tables.
|
| IStratiAmountSelector |
Functional interface to write custom logic for selecting the amount of strati
for a dataset.
|
| IStratiAssigner |
Interface to write custom Assigner for datapoints to strati.
|
| IStratiFileAssigner |
Interface to implement custom Stratum assignment behavior.
|
| ISupervisedLearnerEvaluatorFactory<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends I>> |
|
| ITableGeneratorandCompleter<I,S,P> |
|
| ITimeSeriesDataset |
|
| ITimeSeriesInstance |
|
| IVectorDyad |
|
| JaccardScore |
|
| JaccardScore |
|
| KendallsTauDyadRankingLoss |
Computes the rank correlation measure known as Kendall's tau coefficient, i.e.
|
| KendallsTauOfTopK |
Calculates the kendalls-tau loss only for the top k dyads.
|
| KmeansSampling<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance & org.apache.commons.math3.ml.clustering.Clusterable,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>> |
Implementation of a sampling method using kmeans-clustering.
|
| KmeansSamplingFactory<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance & org.apache.commons.math3.ml.clustering.Clusterable,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<I>> |
|
| KMeansStratiAssigner |
Cluster the data set with k-means into k Clusters, where each cluster stands
for one stratum.
|
| LabelBasedStratifiedSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> |
|
| LabelBasedStratifiedSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> |
|
| LabeledInstanceSchema |
|
| LabelRankingAttribute |
|
| LabelRankingAttributeValue |
|
| LatexDatasetTableGenerator |
|
| LCNetClient |
|
| LCNetExtrapolationMethod |
This class represents a learning curve extrapolation using the LCNet
from pybnn.
|
| LearnerEvaluatorConstructionFailedException |
|
| LearnerRunReport |
|
| LearningCurveExtrapolatedEvent |
|
| LearningCurveExtrapolationEvaluator |
Evaluates a classifier by predicting its learning curve with a few
anchorpoints.
|
| LearningCurveExtrapolationEvaluatorFactory |
|
| LearningCurveExtrapolationMethod |
Functional interface for extrapolating a learning curve from anchorpoints.
|
| LearningCurveExtrapolator |
Abstract class for implementing a learning curve extrapolation method with
some anchor points.
|
| LinearCombinationConstants |
This class contains required constant names for the linear combination
learning curve.
|
| LinearCombinationExtrapolationMethod |
This class describes a method for learning curve extrapolation which
generates a linear combination of suitable functions.
|
| LinearCombinationFunction |
This is a basic class that describes a function which is a weighted
combination of individual functions.
|
| LinearCombinationLearningCurve |
The LinearCombinationLearningCurve consists of the actual linear combination
function that describes the learning curve, as well as the derivative of this
function.
|
| LinearCombinationLearningCurveConfiguration |
A configuration for a linear combination learning curve consists of
parameterizations for at least one linear combination function.
|
| LinearCombinationParameterSet |
This class encapsulates all parameters that are required in order to create a
weighted linear combination of parameterized functions.
|
| LocalCaseControlSampling |
|
| LocalCaseControlSamplingFactory |
|
| MajorityClassifier |
|
| MapInstance |
|
| MathUtil |
Utility class consisting of mathematical utility functions.
|
| MCCVSplitEvaluationEvent |
|
| MeanSquaredError |
|
| MLEvaluationUtil |
|
| MoebiusTransformOWAValueFunction |
|
| MonteCarloCrossValidationEvaluator |
|
| MonteCarloCrossValidationEvaluatorFactory |
Factory for configuring standard Monte Carlo cross-validation evaluators.
|
| MonteCarloCrossValidationSplitSetGenerator<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> |
A DatasetSplitSetGenerator that create k independent splits of the given dataset.
|
| MultiLabelAttribute |
|
| MultiLabelAttributeValue |
|
| MultiLabelClassification |
|
| MultiLabelClassificationPredictionBatch |
|
| MySQLDatasetMapper |
|
| NDArrayTimeseries |
|
| NDArrayTimeseriesAttribute |
Describes a time series type as an 1-NDArray with a fixed length.
|
| NDArrayTimeseriesAttributeValue |
|
| NDCGLoss |
The Normalized Discounted Cumulative Gain for ranking.
|
| NearestNeighborClassifier |
K-Nearest-Neighbor classifier for time series.
|
| NearestNeighborClassifier.VoteType |
Votes types that describe how to aggregate the prediciton for a test instance on its nearest neighbors found.
|
| NearestNeighborLearningAlgorithm |
Training algorithm for the nearest neighbors classifier.
|
| NegIdentityInpOptLoss |
Loss function for PLNet input optimization that maximizes the output of a PLNet.
|
| NoneFittedFilterExeception |
|
| NumericAttribute |
|
| NumericAttributeValue |
|
| OpenMLDatasetDescriptor |
|
| OpenMLDatasetReader |
|
| OSMAC<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> |
|
| OSMACSamplingFactory |
|
| OWARelevanceLoss |
|
| PairWisePreferenceToBinaryClassificationFilter |
|
| ParametricFunction |
This is a basic class that describes a function that can be parameterized with a set of parameters.
|
| PilotEstimateSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> |
|
| PLNetDyadRanker |
A dyad ranker based on a Plackett-Luce network.
|
| PLNetInputOptimizer |
Optimizes a given loss function ( InputOptimizerLoss) with respect to the input of a PLNet using gradient descent. |
| PLNetLoss |
Implements the negative log likelihood (NLL) loss function for PL networks as described in [1]
|
| PointWiseLearningCurve |
This class represents a learning curve that gets returned by the
LCNet from pybnn
|
| PolynomialOWAValueFunction |
|
| Precision |
|
| Prediction |
|
| PredictionBatch |
|
| PredictionDiff<E,A> |
|
| PreTrainedPredictionBasedClassifierEvaluator |
This evaluator can be used to compute the performance of a pre-trained classifier on a given validation dataset
|
| ProblemInstance<I> |
|
| PrototypicalPoolBasedActiveDyadRanker |
A prototypical active dyad ranker based on the idea of uncertainty sampling.
|
| RandomHoldoutSplitter<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
This splitter just creates random split without looking at the data.
|
| RandomlyRankedNodeQueue<N,A,V extends java.lang.Comparable<V>> |
A node queue for the best first search that inserts new nodes at a random
position in the list.
|
| RandomlyRankedNodeQueueConfig<T> |
|
| RandomPoolBasedActiveDyadRanker |
A random active dyad ranker.
|
| Ranking<O> |
|
| RankingForGroup<C,O> |
RankingForGroup.java - saves a solution ranking for a group identified by thier group
|
| RankingPredictionBatch |
|
| RankLoss |
|
| Recall |
|
| ReproducibleSplit<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
|
| ReservoirSampling |
Implementation of the Reservoir Sampling algorithm(comparable to a Simple
Random Sampling for streamed data).
|
| RootMeanSquaredError |
The root mean squared loss function.
|
| SampleComplementComputer |
|
| SampleElementAddedEvent |
|
| SAX |
|
| ScikitLearnWrapper |
Wraps a Scikit-Learn Python process by utilizing a template to start a classifier in Scikit with the given classifier.
|
| ScikitLearnWrapper.ProblemType |
|
| SetOfObjectsAttribute<O> |
|
| SetOfObjectsAttributeValue<O> |
|
| SFA |
|
| Shapelet |
Implementation of a shapelet, i. e. a specific subsequence of a time series
representing a characteristic shape.
|
| ShotgunEnsembleClassifier |
Implementation of Shotgun Ensemble Classifier as published in "Towards Time
Series Classfication without Human Preprocessing" by Patrick Schäfer (2014).
|
| ShotgunEnsembleLearnerAlgorithm |
Implementation of Shotgun Ensemble Algorihm as published in "Towards Time
Series Classfication without Human Preprocessing" by Patrick Schäfer (2014).
|
| ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig |
|
| SimpleRandomSampling<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
|
| SimpleRandomSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> |
|
| SimplifiedTimeSeriesLoader |
Time series loader class which provides functionality to read datasets from
files storing into simplified, more efficient time series datasets.
|
| SingleEvaluationAggregatedMeasure<E,A> |
|
| SingleLabelClassification |
|
| SingleLabelClassificationPredictionBatch |
|
| SingleRandomSplitClassifierEvaluator |
|
| SlidingWindowBuilder |
|
| SparseDyadRankingInstance |
A dyad ranking instance implementation that assumes the same instance for all
dyads contained in its ordering.
|
| SparseInstance |
|
| SparseInstance.ENullElement |
Determines a default interpretation of values not contained in the map of attributes.
|
| SplitterUtil |
|
| SquaredError |
Measure computing the squared error of two doubles.
|
| StratifiedFileSampling |
|
| StratifiedSampling<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
Implementation of Stratified Sampling: Divide dataset into strati and sample
from each of these.
|
| StratifiedSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> |
|
| StringAttribute |
|
| StringAttributeValue |
|
| SubsetZeroOneMassFunction |
|
| SupervisedLearnerExecutor |
|
| SystematicFileSampling |
File-level implementation of Systematic Sampling: Sort datapoints and pick
every k-th datapoint for the sample.
|
| SystematicSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> |
Implementation of Systematic Sampling: Sort datapoints and pick every k-th
datapoint for the sample.
|
| SystematicSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> |
|
| Table<I,S,P> |
Table.java - This class is used to store probleminstance and their according solutions and
performances for that solution.
|
| ThresholdComputationFailedException |
|
| TimeSeriesBatchLoader |
BatchLoader
|
| TimeSeriesDataset |
Time Series Dataset.
|
| TimeSeriesDataset2 |
Dataset for time series.
|
| TimeSeriesFeature |
Class calculating features (e. g. mean, stddev or slope) on given
subsequences of time series.
|
| TimeSeriesFeature.FeatureType |
Feature types used within the time series tree.
|
| TimeSeriesInstance |
TimeSeriesInstance
|
| TimeSeriesLengthException |
Exception class encapsultes faulty behaviour with length of time series.
|
| TimeSeriesLoadingException |
Exception thrown when a time series dataset could not be extracted from an
external data source (e. g. a file).
|
| TimeSeriesUtil |
Utility class for time series operations.
|
| TopKOfPredicted |
Calculates if the top-k dyads of the predicted ranking match the top-k dyads
of the actual ranking.
|
| TrainPredictionBasedClassifierEvaluator |
|
| TrainTestSplitEvaluationCompletedEvent<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends I>> |
|
| TrainTestSplitEvaluationFailedEvent<I extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance,D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends I>> |
|
| TrueNegatives |
|
| TruePositives |
|
| TSLearningProblem |
|
| TypelessPredictionDiff |
This is a helper class with which one can create a prediction diff object without caring about the types of ground truths and predictions.
|
| UCBPoolBasedActiveDyadRanker |
A prototypical active dyad ranker based on the UCB decision rule.
|
| UncheckedJaicoreMLException |
|
| WaitForSamplingStepEvent |
|
| ZeroOneLoss |
|
| ZTransformer |
|